Eating eggplants as a cucurbit feeder: Dietary shifts affect the gut microbiome of the melon fly Zeugodacus cucurbitae (Diptera, Tephritidae)

Abstract While contemporary changes in feeding preferences have been documented in phytophagous insects, the mechanisms behind these processes remain to be fully clarified. In this context, the insect gut microbiome plays a central role in adaptation to novel host plants. The cucurbit frugivorous fruit fly Zeugodacus cucurbitae (Diptera, Tephritidae) has occasionally been reported on “unconventional” host plants from different families, including Solanaceae. In this study, we focus on wild parental (F0) adults and semiwild first filial (F1) larvae of Z. cucurbitae from multiple sites in La Réunion and explore how the gut microbiome composition changes when this fly is feeding on a noncucurbit host (Solanum melongena). Our analyses show nonobvious gut microbiome responses following the F0–F1 host shift and the importance of not just diet but also local effects, which heavily affected the diversity and composition of microbiomes. We identified the main bacterial genera responsible for differences between treatments. These data further stress the importance of a careful approach when drawing general conclusions based on laboratory populations or inadequately replicated field samples.


| INTRODUCTION
Insects are the most diverse group of Eukaryotes (Forister et al., 2015) with a vast variety of species being phytophagous and functionally classified in polyphagous, oligophagous, and monophagous when feeding on plants from multiple families, a single family or a single species, respectively (A. R. Clarke, 2017). Contemporary host plant feeding preferences are generally well defined but shifts in host plant preferences have been reported in a variety of insects, such as lepidopterans, beetles, and grasshoppers (Adams et al., 2013;Brown et al., 2014;Rosenberger et al., 2018;Singer & Parmesan, 2021;Sword et al., 2005). The shift toward novel hosts results in ecological niche expansion and subsequent adaptation to the new host can promote genetic divergence between populations, possibly with the evolution of host races and eventually of new species (Feder et al., 1988;Tilmon, 2008). Host races have been reported in a variety of insects, such as beetles and grasshoppers (Lefort et al., 2014;Sword et al., 2005), and a classical textbook example of the evolution of host MicrobiologyOpen. 2022;11:e1307. www.MicrobiologyOpen.com races is Rhagoletis pomonella (Diptera, Tephritidae), where host-shifts from hawthorn to apples have led to the evolution of genetically divergent populations with different feeding preferences (Feder et al., 1988). Host plant shift or expansion may also favor geographic range expansion (and vice versa) due to the possibility of occupying novel ecological niches and a wider geographic distribution (Hood et al., 2020;Lefort et al., 2014;Rosenberger et al., 2018;Singer & Parmesan, 2021). These processes are of major significance in agronomy and conservation biology as they can promote the emergence of new invasive species and agricultural pests (Brown et al., 2014;Lefort et al., 2014;Lu et al., 2011). The host shift of R. pomonella is considered the main cause of the expansion of this species in the Northwest Pacific (Hood et al., 2020).
While changes in insect feeding preferences have been documented in phytophagous insects, the mechanisms behind these processes remain to be clarified. In this context, the insect gut microbiome plays a central role in adaptation to novel host plants as it is of crucial importance for the complex interactions with insect metabolic pathways, which ultimately affect insect fitness (Hammer & Bowers, 2015;Zilber-Rosenberg & Rosenberg, 2008). The gut microbiome of phytophagous insects can help to (1) break down the complex polysaccharides of the host plant cell wall, and (2) supplement nitrogen, vitamins, and sterols to nutritionally poor diets (Ben-Yosef et al., 2010, 2014Douglas, 2009), and (3) detoxify host plant allelochemicals (Hammer & Bowers, 2015) and insecticides (Ishigami et al., 2021;Kikuchi et al., 2012). For example, the symbiotic bacterium "Candidatus Erwinia dacicola" is essential for the metabolism of larvae of the olive fly Bactrocera oleae (Rossi, 1790) (Diptera, Tephritidae) as it allows them to feed on unripe olives rich in allelochemicals (Ben-Yosef et al., 2015;Pavlidi et al., 2017). The geographic range expansion of the kudzu bug (Megacopta cribaria, Hemiptera: Plataspidae) in the United States has been related to genomic mutations in its symbiont Ishikawaella, which allowed the insect to attack soybean as a novel host plant (Brown et al., 2014). Likewise, the invasive spread of some bark beetle species (Coleoptera: Scolytinae) has been associated with compositional changes in their microbial and fungal symbionts (Adams et al., 2013;Lu et al., 2011).
Tephritid fruit flies are a diverse group of flies with herbivorous larvae, with several species being notorious agricultural pests (Norrbom et al., 1999 Meyer et al., 2015;Dhillon et al., 2005;Hafsi et al., 2016;Moquet et al., 2021). This, together with observed geographic range expansion in recent decades (De Meyer et al., 2015), raises concerns about the invasion risk of this species, which might be polyphagous rather than oligophagous. (Cucurbitaceae host, "Cu"), or Solanum melongena L. (Solanaceae host, "So"). The females were let to oviposit and the resulting six groups of third instar F 1 larvae (one group for each combination of site and host), were subsampled and subjected to gut microbiome profiling.
Core (stable associates) bacterial genera and ASVs were identified using the abundance-ubiquity method with a 50% minimal ubiquity threshold. This statistic evaluates whether a bacterial taxon is not more abundant than expected for its ubiquity. Significant deviations from expectation indicate that a bacterial taxon is not a stable core member (Hester et al., 2016). Statistical analyses were performed in R unless stated otherwise. Permutational analysis of variance (PERMANOVA) was conducted with PRIMER v7 (K. R. Clarke & Gorley, 2015) using 9999 unrestricted permutations of raw data).

| Microbiome diversity and predictive functional profiling
Three α diversity metrics were calculated: the Abundance coverage estimator (ACE) to assess ASV richness, the Inverse Simpson index (ISI) to assess ASV evenness, and Faith's phylogenetic diversity (FPD) to investigate phylogenetic richness. To evaluate differences in microbial α diversity between larvae raised on different host plants and/or between sites, we used two-way ANOVAs (Underwood, 1997) which for F 1 larvae included Site (BP and M) as a random factor and Host Plant (C. grandis, C. sativus, and S. melongena) as a fixed orthogonal factor and for F 0 adults included Site (BP and M) as a random factor and Parental Group (groups 1, 2, and 3) as a random factor nested in Site. ANOVA was implemented using the GAD package (Sandrini-Neto & Camargo, 2015). Count data from which diversity metrics were calculated were not normalized as all rarefaction plots reached a plateau ( Figure A1). To ensure homoscedasticity, a log transformation was applied to the ISI and a fourth root transformation was applied to the ACE and FPD. Cochran's C tests were used to test for homogeneity of variances with the GAD package (Fox, 2006). Pairwise comparisons were done by using an Generalized UniFrac distances using the d5 matrix and unweighted UniFrac distances were calculated as β diversity metrics (Chen et al., 2012). As UniFrac distances take into account the phylogenetic relationships, we constructed a midpoint-rooted maximum likelihood tree of the bacterial relationships using a general time-reversible substitution model in the program Fasttree (Price et al., 2009). Bacterial 16S sequences were aligned with the DECIPHER algorithm (Wright, 2015).
Differences in microbiome β diversity between larvae raised on different host plants and/or between sites were tested using a two-way PERMANOVA (Anderson, 2017) with Site (BP and M) as a random factor and host plant (C. grandis, C. sativus, and S. melongena) as a fixed orthogonal factor. The false discovery rate (FDR) correction (Benjamini & Hochberg, 1995) with experiment-wise p < 0.05 was used to correct for multiple testing. PERMANOVA was also used to estimate the components of variance explained by each factor. Differences between larvae raised on different host plants were visualized with a principal coordinate analysis (PCoA) and 95% confidence ellipses were drawn using the ggplot2 package (Wickham & Chang, 2016).
To test for a differential abundance of microbial genera (i.e., genera with relatively more sequences assigned to them) among larvae raised on different host plants and from different sites, we used ALDEx2 HENDRYCKS ET AL.
| 3 of 16 (Fernandes et al., 2013). ASVs that could not be classified were assigned to distinct, unidentified genera. Genera that showed differential abundance between two treatments with an effect size difference between 1 and −1 were filtered out to reduce the false positive rate (Gloor, 2015). Significance was assessed by both the Welch t test and the Wilcoxon rank-sum test followed by FDR correction with experimentwise p < 0.05 as FDR is better suited for exploratory analyses (Lee & Lee, 2018). We selected 23 genera that showed the greatest differences in ALDEx2 to visualize patterns in heatmaps using the pheatmap package (Kolde, 2015). Clustering of sites and diet sources was done using Euclidean distances. For eight of these genera, we constructed boxplots with ggplot and arranged them in a single figure using the ggarrange function in the ggpubr package (Kassambra & Kassambra, 2020).

| Microbiome composition of adult Z. cucurbitae
Enterobacter, Klebsiella, and Citrobacter were identified as core genera sensu Hester et al. (2016). In general, microbiomes of adult flies had a mean ASV richness of 26.12 (SD = 8.12) and a mean FPD of 5.97 (SD = 1.01) for adult microbiomes. They were also more uneven (more dominated by few abundant taxa) with an average ISI of 4.95 (SD = 2.25).
No differences in any diversity metric were found between F 0 adults from different sites or different parental groups (Table A2). PERMANOVA of parental F 0 adult fly microbiomes (Tables A3 and A4) revealed significant differences between parental groups when species presence/absence (unweighted UniFrac distances) was considered (with one out of six significant post hoc comparisons). However, when relative abundances were taken into account and more weight was given to highly abundant taxa (generalized UniFrac distances), the F 0 parental groups were not significantly different from each other. No significant differences between sites were found for both distances (Table A3). The PCoA plots ( Figure A2), could not resolve distinct parental clusters for both unweighted and generalized UniFrac distances.

| Microbiome composition of larval Z. cucurbitae
Across all host plants, we identified seven core genera (Hester et al., 2016): Acinetobacter, Enterobacter, Klebsiella, Paenibacillus, Pseudomonas, Stenotrophomonas, and Sphingobacterium. Larval microbiomes showed a high ASV richness (mean = 145.36, SD = 75.56) but low phylogenetic diversity (mean = 13.51, SD = 4.58). Despite their high ASV richness, larval microbiomes were dominated by very few ASV as the ISI had a mean of 7.97 (SD = 6.21). The log-transformed ISI did not show consistent patterns across sites or host plants ( Figure A3 and Table A5). There was a significant interaction between the host plant and site in the fourth root transformed ACE, the logtransformed ISI, and the fourth root transformed FPD (Table 1).
The α diversity indices did not show consistent patterns across sites and host plants. ASV richness as estimated by ACE and FPD showed significantly higher values for Co in M, but not in BP. Conversely, evenness showed significantly higher values for So in BP, but not in M ( Figure A3 and Table A5).
PERMANOVA of the generalized and unweighted UniFrac distances revealed a significant interaction for β diversity between host plant and site (Table 2). However, pairwise comparisons did not provide additional information as all were significant (  One of the objectives of this study was to explore changes in the microbiome composition of a cucurbit-feeder fly feeding on a noncucurbit host (S. melongena, Solanaceae) with the expectation that the shift to a noncucurbit diet would produce major and consistent changes in its microbiome. This only partially happened.
First, changes in the microbiome composition related to host plant diet mostly concerned less abundant taxa as differences were observed mainly from the analysis of presence/absence data (unweighted UniFrac distances) rather than from the weighted abundances (generalized UniFrac distances). This is consistent with recent studies on termites and wood-eating cockroaches that also explanation for these results might be that several generations are required before reaching stable and consistent microbiome assemblages in flies shifting to a novel "atypical" diet. A recent study in the whitefly Bemisia tabaci (Hemiptera, Aleyrodidae), for example, found that an initial host switch from watermelon to the less suitable host pepper did not result in major changes in microbiome composition and structure (Santos-Garcia et al., 2020). Yet, major microbiome changes did occur in subsequent generations. Moreover, the same study also showed that the first generation following the host shift had a lower survival rate, which increased again in subsequent generations, suggesting that the microbiota was involved in longerterm adaptation to the new host plant. Similarly, in the diamondback moth (Plutella xylostella, Lepidoptera, Plutellidae) a host shift to novel pea hosts resulted in major microbiome changes only in later generations, not in the first generation (Yang et al., 2020).
Another hypothesis for the lack of straightforward relationships between diet and microbiome composition is that microbiome changes mainly involve bacterial taxa that serve important metabolic functions but which are so rare that they remain undetected. Indeed, rare members of microbial communities sometimes perform key functions in these communities (Jousset et al., 2017). Desulfosporinus, for example, represents only 0.006% of reads detected in microbial peatland communities and yet it contributes the most to sulfate reduction (Pester et al., 2010).
Likewise, the capacity of freshwater microbial communities to degrade pollutants is severely reduced when rare taxa disappear (Delgado-Baquerizo et al., 2016). So, rare taxa may support a community with a wide range of metabolic functions that might only be important under specific circumstances (Jousset et al., 2017) such as the use of an unconventional host plant species.
We also need to consider that changes in host plant use might not necessarily translate into major compositional changes in microbiome assemblages but rather result in changes in the gene expression patterns of the "holobiont" sensu Margulis and Fester (1991) (i.e., the insect and its microbiota living on the host plant), which is the central unit of symbiogenesis and evolution (Guerrero et al., 2013). Symbiont microbial pectinases complement the insect endogenous cellulases and xylanases in herbivorous beetles (Cassidinae) and the pectinolytic range of symbiotic bacteria of the genus Stammera has been associated with the diversity of host plants that can be attacked by these beetles (Salem et al., 2020). Accordingly, the flexibility of insect and microbiome gene expression patterns could provide a complementary/alternative explanation to the complex relationships observed between microbiome assemblages, feeding preferences, and range expansion of Z. cucurbitae.
Additionally, differences in microbiome composition and structure between larvae feeding on the noncucurbit host could have also been affected by differences in their parental microbiomes, as significant heterogeneity was detected between the microbiomes of the parental lines and since in tephritids, at least part of the microbiome is vertically transmitted (Behar et al., 2008).
One potential drawback of our study is that we did not investigate the effect of captivity on the fly microbiome, especially with regard to vertical transmission from adults to larvae. Although we used wild populations for our experiment,  Augustinos et al., 2019;Morrow et al., 2015;Ras et al., 2017;Sacchetti et al., 2019). Moreover, changes in the larval microbiome due to captivity can occur in even relatively short periods. Majumder et al. (2022), for example, already detected changes in larval microbiomes after a single generation in captivity. Interestingly, changes in the adult microbiome took more generations before they started to occur. This suggests that rapid changes in the larval microbiome due to captivity are more due to environmental differences between the captive and natural environment experienced by the larvae (not the adult), such as diet, rather than due to a loss of vertically transmitted symbionts or genetic changes (Majumder et al., 2020). Indeed, prior studies have shown that the larval microbiome undergoes significant changes when larvae are reared on an artificial diet rather than a more natural diet. Likewise, studies on zoo animals have shown that exposing animals to more natural conditions and bacterial sources keeps their microbiome more similar to the microbiomes of their wild relatives (Loudon et al., 2014). Because of this, we do not consider this a major drawback of our study as larvae in our experiment were reared on a natural diet and exposed to a more natural environment, reducing, therefore, the impact of captivity on larval microbiomes and keeping them more representative of the natural conditions.  T A B L E A6 A posteriori comparisons between (A) unweighted UniFrac and (B) generalized UniFrac distances (see Table 2